code: def evaluate(model, loss_fn, dataloader, device="cpu"):
      loss = 0
      f1 = 0
      auc = 0
      rap = 0
      num_blocks = 0
      for input_nodes, edge_subgraph, blocks in dataloader:
          blocks = [b.to(device) for b in blocks]
          edge_subgraph = edge_subgraph.to(device)
          u_feat = blocks[0].srcdata["feat"]["u"]
          v_feat = blocks[0].srcdata["feat"]["v"]
          f_feat = blocks[0].edges["forward"].data["feat"]
          b_feat = blocks[0].edges["backward"].data["feat"]
          labels = edge_subgraph.edges["forward"].data["label"].long()
          logits = model(
              edge_subgraph, blocks, f_feat, b_feat, u_feat, v_feat
          )
  
          loss += loss_fn(logits, labels).item()
          f1 += f1_score(labels.cpu(), logits.argmax(dim=1).cpu())
          auc += roc_auc_score(
              labels.cpu(), logits[:, 1].detach().cpu()
          )
          pre, re, _ = precision_recall_curve(
              labels.cpu(), logits[:, 1].detach().cpu()
          )
          rap += re[pre > args.precision].max()
          num_blocks += 1
  
      return (
          rap / num_blocks,
          f1 / num_blocks,
          auc / num_blocks,
          loss / num_blocks,
      )
  
file_path: dgl/examples/pytorch/gas/main_sampling.py
imports: import argparse
  import dgl
  import torch
  import torch.nn.functional
  import torch.optim
  from dataloader import GASDataset
  from model_sampling import GAS
  from sklearn.metrics import f1_score, precision_recall_curve, roc_auc_score
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, loss_fn, dataloader, device) and returns: unknown,  unknown,  unknown,  unknown